Genes, Economics, and Happiness - Columbia University · 2019-01-10 · e ect on happiness, or only...

Submitted to Econometrica

Genes, Economics, and Happiness

Jan-Emmanuel De Neve, Nicholas A. Christakis, James

H. Fowler and Bruno S. Frey

February 8, 2011

Submitted to Econometrica

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

GENES, ECONOMICS, AND HAPPINESS1

Jan-Emmanuel De Nevea,2, Nicholas A. Christakisb, James H.

Fowler c and Bruno S. Freyd

A major finding from research into the sources of subjective well-being is that

individuals exhibit a “baseline” level of happiness. We explore the influence of

genetic variation by employing a twin design and genetic association study. We

first show that about 33% of the variation in happiness is explained by genes.

Next, using two independent data sources, we present evidence that individuals

with a transcriptionally more efficient version of the serotonin transporter gene

(SLC6A4 ) report significantly higher levels of life satisfaction. These results are

the first to identify a specific gene that is associated with happiness and suggest

that behavioral models benefit from integrating genetic variation.

Keywords: Subjective Well-Being, Neuroeconomics, Twin Design Study, Ge-

netic Association.

1The authors thank Dan Benjamin, Chris Chabris, Chris Dawes, Pete Hatemi, David

Laibson, Richard Layard, Jaime Settle, Albert Vernon Smith, and Piero Stanig. This

paper also benefited from comments at the Integrating Genetics and Social Sciences

conference (Boulder, CO) and the UCL Institute of Neurology seminar. Research was

supported by National Institute on Aging grant P-01 AG-031093 and National Science

Foundation grant SES-0719404.2 De Neve benefited from the generous hospitality of the Institute for Empirical Re-

search in Economics (IEW) at the University of Zurich and the Center for Research in

Economics, Management and the Arts (CREMA).aDepartment of Government, London School of Economics, London, United Kingdom,

[email protected] Medical School, Harvard University, Cambridge, United States,

[email protected] of Medicine and Division of Social Sciences, University of California at San

Diego, La Jolla, United States, [email protected] for Empirical Research in Economics, University of Zurich, Switzerland,

[email protected]

1ectaart.cls ver. 2006/04/11 file: GenesEconomicsHappiness_Ecta_8Feb11.tex date: February 8, 2011

http://www.econometricsociety.org/

mailto:[email protected]




2

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

We all know from comparing siblings that people are born different, and

these differences are then amplified by subsequent experience. So our

happiness depends on our genes and our experience (past and present).

Any social reformer has to be mainly interested in the role of experience

since that is all that we can change. But we will never understand that

bit unless we understand the complete reality, and the complete reality

includes a strong role for the genes.

—Prof. Lord Richard Layard, Lionel Robbins Memorial Lecture, LSE,

February 27, 2003.

1. INTRODUCTION

Happiness research has become one of the liveliest subjects in economics

in recent years1. Its main goal is to explain the determinants of individ-

ual life satisfaction or subjective well-being (often loosely called happiness).

Economists have mainly dealt with economic influences, in particular, in-

come and its distribution, labor market regulation, unemployment and in-

flation. For example, Di Tella, MacCulloch, and Oswald (2001) used happi-

ness surveys to determine the welfare costs of inflation and unemployment,

showing that unemployment depresses reported well-being more than does

inflation. In fact, their longitudinal study of life satisfaction self-reports en-

abled these authors to estimate that people would trade off a 1 percentage-

point increase in the unemployment rate for a 1.7 percentage-point increase

in the inflation rate. Systematic influences on life satisfaction have also

been found for socio-demographic factors (age, gender, race, marital status,

children, and social networks) as well as for political and cultural factors

1Books are e.g. Kahneman, Diener, and Schwarz (1999), Graham and Pettinato (2002),Frey and Stutzer (2002a), Van Praag and Ferrer-I-Carbonell (2004), Layard (2005), orFrey (2008); articles are e.g. Easterlin (1974), Clark and Oswald (1996), Frey and Stutzer(2002b), Di Tella, MacCulloch, and Oswald (2003), Luttmer (2005), Di Tella and Mac-Culloch (2006), Rayo and Becker (2007), Dolan, Peasgood, and White (2008), Fowler andChristakis (2008), Urry, Nitschke, Dolski, Jackson, Dalton, Mueller, Rosenkranz, Ryff,Singer, and Davidson (2004) or Clark, Frijters, and Shields (2008).

ectaart.cls ver. 2006/04/11 file: GenesEconomicsHappiness_Ecta_8Feb11.tex date: February 8, 2011

3

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

(such as democracy, decentralization, and religiosity). While variables like

socio-economic status, income, marriage, education, and religiosity are sig-

nificantly associated with individual happiness, none typically accounts for

more than 3% of the variation (Layard, 2005; Frey, 2008). Moreover, changes

in these variables appear to yield only short term changes to happiness. For

example, the “Easterlin Paradox” (Easterlin 1974, 2004, Clark, Frijters and

Shields 2008) suggests that increases in real income either have no lasting

effect on happiness, or only a quite small one (Stevenson and Wolfers, 2008).

The reason appears to be that happiness levels tend to revert toward what

psychologists describe as a “set point” or “baseline” of happiness that is

influenced by personality and genetic predispositions (Kahneman, Diener,

and Schwarz, 1999; Diener and Lucas, 1999).

Although previous studies have shown that baseline happiness is signif-

icantly heritable (Lykken and Tellegen, 1996), none has so far identified a

specific gene associated with subjective well-being. Here, we replicate the

earlier work showing that happiness is significantly influenced by genetic

variation in a nationally-representative sample, and then we present evi-

dence of a specific gene that is associated with life satisfaction. We find

that individuals with a transcriptionally more efficient version of the sero-

tonin transporter gene (SLC6A4, also known as 5-HTT) are significantly

more likely to report higher levels of life satisfaction and we replicate this

association on an independent data set. This combination of economics and

genetics is of rising salience (Benjamin, Chabris, Glaeser, Gudnason, Har-

ris, Laibson, Launer, and Purcell, 2007; Beauchamp, Cesarini, Johannesson,

van der Loos, Koellinger, Broenen, Fowler, Rosenquist, Thurik, and Chris-

takis, 2010).

Before we detail our genetic association approach and results, we explore

the general influence that genes may have on happiness through a twin

study design. A growing number of studies use twin research techniques to


4

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

gauge the relative importance of genetic and environmental influences on

economic behaviors (e.g. Cesarini, Dawes, Johannesson, Lichtenstein, and

Wallace (2009), Fowler, Dawes, and Christakis (2009)). We estimate the

heritability of subjective well-being at 33%, indicating that about one-third

of the variance in individual life satisfaction can be attributed to genetic

influences.

Although twin studies are an important step in establishing the influence

of genes in subjective well-being, they do not identify the specific genes in-

volved. The increasing availability of genotypic information now allows us

to test hypotheses about targeted genes and their effects. One place to start

the search for such genes is among those that have already been shown to

account for variation in emotional states. Among these, SLC6A4 is a prime

candidate. The SLC6A4 gene encodes a transporter in the cell wall that

absorbs serotonin into the presynaptic neuron in parts of the brain that

influence mental states (Hariri, Mattay, Tessitore, Kolachana, Fera, and

Goldman, 2002; Bertolino, Arciero, Rubino, Latorre, Candia, and Mazzola,

2005; Heinz, Braus, Smolka, Wrase, Puls, Hermann, and et al., 2005; Canli

and Lesch, 2007). SLC6A4 has been studied for more than twenty years and

much is known about the way different versions of this gene influence tran-

scription, metabolism, and signal transfers between neurons, all of which

may influence personality. In particular, less transcriptionally efficient vari-

ants of this gene have been shown to moderate the influence of life stress

on depression (Caspi, Sugden, Moffitt, Taylor, Craig, Harrington, McClay,

Mill, Martin, Braithwaite, and Poulton, 2003); and the more transcription-

ally efficient alleles have been linked to optimism (Fox, Ridgewell, and Ash-

win, 2009). As a result, economists have specifically identified SLC6A4 as

a candidate gene for further study (Benjamin, Chabris, Glaeser, Gudnason,

Harris, Laibson, Launer, and Purcell, 2007).

Using data from two independent sources, the National Longitudinal


5

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

Study of Adolescent Health (Add Health) and the Framingham Heart Study

(FHS), we analyze the relationship between variants of SLC6A4 and life

satisfaction. We find evidence of significant association in both data sets,

suggesting that the SLC6A4 gene may play a role in explaining subjective

well-being. While we do not claim that SLC6A4 determines happiness, nor

do we exclude the possibility that several other genes may also play a role,

we do think that the results suggest at least one possible causal pathway

able to account for the influence of genes on happiness. And to our knowl-

edge, this is the first study to identify a specific gene involved in the process.

This in turn has implications for how economists think about the determi-

nants of utility, and the extent to which exogenous shocks might affect and

individual’s well-being.

2. THE ADD HEALTH DATA

This research is based on genetic and survey data collected as part of the

National Longitudinal Study of Adolescent Health (Add Health). The study

was initially designed to explore the health-related behavior of adolescents in

grades 7 through 12, but it has been employed widely across disciplines and

has made recent contributions in economics (Echenique, Fryer, and Kauf-

man, 2006; Echenique and Fryer, 2007; Alcott, Karlan, Mobius, Rosenblat,

and Szeidl, 2007; Norton and Han, 2009). In the first wave of the Add Health

study (1994–1995) 80 high schools were selected from a sampling frame of

26,666 based on their size, school type, census region, level of urbanization,

and percent of the population that was white. Participating high schools

were asked to identify junior high or middle schools that served as feeder

schools to their school. This resulted in the participation of 145 middle, ju-

nior high, and high schools. From those schools, 90,118 students completed

a 45-minute questionnaire and each school was asked to complete at least

one School Administrator questionnaire. This process generated descriptive


6

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

information about each student, the educational setting, and the environ-

ment of the school. From these respondents, a core random sample of 12,105

adolescents in grades 7-12 were drawn, along with several over-samples, to-

taling more than 27,000 adolescents. These students and their parents were

administered in-home surveys in the first wave.

Wave II (1996) was comprised of another set of in-home interviews of more

than 14,738 students from the Wave I sample and a follow-up telephone

survey of the school administrators. Wave III (2001–02) consisted of an

in-home interview of 15,170 Wave I participants. Finally, Wave IV (2008)

consisted of an in-home interview of 15,701 Wave I participants. The result

of this sampling design is that Add Health is a nationally representative

study. Women make up 49% of the study’s participants, Hispanics 12.2%,

Blacks 16.0%, Asians 3.3%, and Native Americans 2.2%.2 Participants in

Add Health also represent all regions of the United States: the Northeast

makes up 17% of the sample, the South 27%, the Midwest 19%, and the

West 17%.

In Wave I of the Add Health study, researchers created a sample of sib-

ling pairs including all adolescents that were identified as twin pairs, half-

siblings, or unrelated siblings raised together. Twin pairs were sampled with

certainty. The sibling-pairs sample is similar in demographic composition to

the full Add Health sample (Jacobson and Rowe, 1998). The number of iden-

tical (monozygotic) and non-identical (dizygotic) twins who participated in

Wave III was 1,098 (434 MZ and 664 DZ), with 872 twins (434 MZ and 438

DZ) in same sex pairs. The Add Health data has been widely used for twin

studies (Harris, Halpern, Smolen, and Haberstick, 2006; Fowler, Baker, and

Dawes, 2008).

Allelic information for a number of genetic markers were collected for

2,574 individuals as part of Wave III. The genes chosen for inclusion in the

2A breakdown for those providing DNA samples is presented in the Appendix.


7

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

study are known to affect brain development, neurotransmitter synthesis

and reception, and hormone regulation. Allelic information includes markers

that identify alleles (variants) of the serotonin transporter gene or SLC6A4.

The promotor region of SLC6A4 (called 5-HTTLPR) contains a variable

number tandem repeat (VNTR) sequence that influences transcriptional

activity—the “long” 528 base-pair allele is associated with a much higher

basal activity than the “short” 484 base-pair allele. Allele frequency for

the short allele is 43% and for the long allele is 57%. Details of the DNA

collection and genotyping process are available at the Add Health website

(Add Health Biomarker Team, 2007).

In Wave III, subjects were asked “How satisfied are you with your life as

a whole?” Answer categories ranged from very dissatisfied, dissatisfied, nei-

ther satisfied nor dissatisfied, satisfied, to very satisfied. Alternative answers

were “refused” or “don’t know” and these were discarded for the purpose

of this study (less than 1% of interviewees gave such a response). This

question and answer formulation is standard in the economics of happiness

literature (Di Tella, MacCulloch, and Oswald, 2001, 2003; Kahneman and

Krueger, 2006; Frey, 2008). The distribution of answers to the life satisfac-

tion question is shown in Appendix. In line with the happiness literature, a

large majority of respondents report being satisfied or very satisfied (Frey

and Stutzer, 2002a). That most people, in fact, report a positive level of

subjective well-being is the object of a paper by Diener and Diener (1996),

where the authors find this distribution to be representative in a wide cross-

national analysis.

3. TWIN DESIGN

3.1. Methods

Twin studies compare the traits, behaviors, and other outcomes (called

“phenotypes”) of twins who share 100% of their genetic material (identical


8

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

or monozygotic twins) to those who share 50% of their genetic material

(fraternal or dizygotic twins) in order to estimate the relative importance

of genetic and environmental influences (Taubman, 1976; Ashenfelter and

Krueger, 1994). If we assume that the influence of the environment on the

phenotype is the same for monozygotic (MZ) and dizygotic (DZ) twins (the

“common environments” assumption), and there are no gene-environment

interactions, then the variance in happiness can be decomposed into additive

genetic effects (A), common or shared environmental influences (C), and

unshared or unique environmental influences (E). The ACE model does not

allow us to observe environmental and genetic influences directly, but it

does allow us to estimate these effects by observing the covariance across

MZ and DZ twins.

Although the assumptions underlying the ACE model are strong, the

method produces results that have been validated in numerous other stud-

ies. For example, studies of twins reared apart generate similar heritability

estimates to those generated by studies of twins raised together (Bouchard,

1998). More recently, Visscher, Medland, Ferreira, Morley, Zhu, Cornes,

Montgomery, and Martin (2006) utilize the small variance in percentage of

shared genes among DZ twins to estimate heritability without using any MZ

twins, and they are able to replicate findings from studies of MZ and DZ

twins reared together. Moreover, personality and cognitive differences be-

tween MZ and DZ twins persist even among twins whose zygosity has been

miscategorized by their parents, indicating that being mistakenly treated

as an identical twin by ones parents is not sufficient to generate a dif-

ference in concordance (Scarr and Carter-Saltzman, 1979; Kendler, Neale,

Kessler, Heath, and Eaves, 1993; Xian, Scherrer, Eisen, True, Heath, Gold-

berg, Lyons, and Tsuang, 2000).

The ACE model can be formally expressed as:


9

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

yij = µ+ Aij + Cj + Eij

where y is the measure of the phenotype, j denotes the family, i denotes

the individual twin in the family, µ is the mean of this phenotype across

all observations, Aij ∼ N(0, σ2A) is the additive genetic component, Cj ∼

N(0, σ2C) is the shared environment component, and Eij ∼ N(0, σ2

E) is the

unshared environment component. Notice that these assumptions imply:

V ar(y) = σ2A + σ2

C + σ2E.

If we further assume that the unshared environment is uncorrelated be-

tween twins (COV (E1j, E2j) = 0), that genes are perfectly correlated be-

tween MZ twins (COVMZ(A1j, A2j) = σ2A), and the covariance between DZ

twins who share half their genes on average is half that of identical twins

(COVDZ(A1j, A2j) = 12σ2

A), then we have two additional equations

COVMZ(y1j, y2j) = σ2A + σ2

C ,

COVDZ(y1j, y2j) =1

2σ2

A + σ2C

The covariance equations reflect the fact that DZ twins share on average

50% of their genes whereas MZ twins share all of their genes. Based on these

equations, we can estimate the ACE model via a random effects regression

model with the 2× 2 variance-covariance matrix specified as:

Ωj =

σ2A + σ2

C + σ2E Rjσ

2A + σ2

C

Rjσ2A + σ2

C σ2A + σ2

C + σ2E

where R is the genetic relatedness of the twin pair equaling 1 for MZ twins

and 12

for DZ twins. We use the variances of the random effects to generate


10

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

estimates of heritability, common environment, and unshared environment.3

To generate the ACE estimates, we use the structural equation model-

ing program OpenMx developed by Neale, Boker, Xie, and Maes (2010).

In addition to estimating ACE models, we estimate all of the possible sub-

models to compare model fit. These include an AE model, which assumes

only genes and unshared environment influence the phenotype (C=0), a CE

model which assumes only common and unshared environment influence

the phenotype (A=0), and an E model (A=0 and C=0). If a submodel fits

better than the general ACE model, this suggests the parameters left out

of the submodel are not significantly contributing to model fit. To compare

the submodels, we use the Akaike Information Criterion (AIC) in maximum

likelihood estimation, where smaller values indicate better fit.

3.2. Twin results

When assessing the role of genetic influences, the first step is to compare

the correlation in phenotype among MZ twin pairs to that of DZ twin pairs.

For life satisfaction, the correlation coefficient for MZ twins is 0.345 and for

DZ twins is 0.129. The difference in correlations is significant (p = 0.032,

one sided). These correlations show that identical twins are significantly

more similar in their level of happiness than fraternal twins, which suggests

that genetic factors might play a role in this trait.

In Table I we report results from several variance decomposition models

described above. Note that the ACE model yields a heritability estimate of

33%, while the estimate for common environment is 0% and the estimate for

unshared environment is 67%. In other words, about a third of the variance

in happiness in our sample can be attributed to variance in genetic factors.

We also examine the submodels and find that the models with lowest AIC all

3They are defined as σ2A

σ2A

+σ2C

+σ2E

, σ2C

σ2A

+σ2C

+σ2E

, and σ2E

σ2A

+σ2C

+σ2E

respectively.


http://openmx.psyc.virginia.edu/

11

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

include A, suggesting that the finding that happiness is heritable is robust

to different model specifications.4.

TABLE I

Summary of ACE twin model results.

Life satisfaction Fit statisticsa2 c2 e2 ep -2ll df AIC diff -2ll diff df p

ACE 0.331 0.000 0.669 4 1878.9 795 288.9 - - -AE 0.331 - 0.669 3 1878.9 796 286.9 0 1 1CE - 0.257 0.743 3 1882.9 796 290.9 4 1 0.05E - - 1 2 1907.2 797 313.2 28.3 2 0

Note: The models consist of additive genetic factors (A), shared or commonenvironmental factors (C), and unshared environmental factors (E). The modelincludes 217 MZ and 219 DZ same-sex twin pairs.

Compared to previous studies of happiness, our heritability estimate of

33% is on the lower end of reported estimates. In fact, the seminal paper

by Lykken and Tellegen (1996) estimated heritability at about 50%, and

subsequent estimates ranged from 38% (Stubbe, Posthuma, Boomsma, and

De Geus, 2005) to 36–50% (Bartels and Boomsma, 2009) to 42–56% (Nes,

Roysamb, Tambs, Harris, and Reichborn-Kjennerud, 2006). However, the

Add Health study includes other questions that suggest the heritability of

happiness rises as people age. The standard life satisfaction question used

in this paper is only asked of Add Health subjects in Wave III (2001–02),

but in other interview waves the following question is asked of participants:

“How often was the following true during the past seven days? You felt

happy.” Answers range from “never or rarely” to “most of the time or all of

the time.” Figure 3 shows the MZ and DZ twin pair correlations of the time

series that combines the “life satisfaction” and “You felt happy” questions.

The basic heritability estimates that result from comparing MZ and DZ

4When we split our twin sample by sex we find that there are significant differencesbetween men and women. As in Table I, Table III in the Appendix shows that the AEmodels fit happiness best according to the AIC values. However, the heritability estimatefor males is 39%, whereas for females it is 26%.


12

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

correlations range from 22% in Wave I (1994) to 54% in Wave IV (2008).

This longitudinal analysis is consistent with a growing body of longitudinal

twin research that shows that the heritability of a number of traits (e.g.

intelligence) increases with age (Plomin, DeFries, McClearn, and McGuffin,

2008). It also shows that the finding that happiness is heritable is robust to

a variety of measures and time periods over the life course. These findings

are generally taken to mean that genes and environment can play differing

roles in explaining experience at different points in the life course.

4. GENETIC ASSOCIATION

Twin studies are important because they allow us to gauge the relative

influence of our genetic makeup on subjective well-being. However, twin

studies do not give insight into which specific genes may be involved in

explaining the heritability of traits. Because Add Health collected a number

of specific genetic markers, it presents us with a unique opportunity to move

beyond a twin design study. Below we introduce some basic concepts in

genetics, our genetic association research design, and present results for our

candidate gene study.

4.1. Basic Concepts in Genetics

Human DNA is composed of an estimated 21,000 genes that form the

blueprint for molecules that regulate the development and function of the

human body. Genes are distinct regions of human DNA that are placed

in the 23 pairs of chains, or chromosomes, that make up all human DNA.

Almost all human cells contain the same DNA they inherited at the moment

of conception.

Individuals inherit one half of their DNA from each parent, with one

copy of each gene coming from the mother and one copy from the father.

Some genes come in different versions, known as “alleles”—for example,


13

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

sickle cell disease results from a particular allele coding for abnormal rather

than normal hemoglobin. Each parent has two separate copies of an allele

at each “locus”, or location, on the chromosome, but each sperm or egg cell

contains only one of these alleles. Thus a child has a 50% chance of receiving

a particular allele from a particular parent. For example, suppose that at

a given locus there are two possible alleles, A and B. If both parents are

“heterozygous” at that locus, meaning they each have an A and a B allele

(AB or BA—order is irrelevant), then a given offspring has a 25% chance of

being “homozygous” for A (AA), a 25% chance of being homozygous for B

(BB) and a 50% chance of being heterozygous (AB or BA). If an individual

is heterozygous at a locus, a “dominant” allele may impose itself on the

“recessive” allele and the expression of the latter allele will not be observed.

Genes transcribe proteins that begin a cascade of interactions that reg-

ulate bodily structure and function. Many of the observable traits and be-

haviors of interest, referred to as “phenotypes” are far downstream from the

original “genotypes” present in the DNA. While in some cases one allele can

single-handedly lead to a disease (such as Sickle Cell Anemia, Huntingtons

disease, or cystic fibrosis), the vast majority of phenotypes are “polygenic”,

meaning they are influenced by multiple genes (Mackay, 2001; Plomin, De-

Fries, McClearn, and McGuffin, 2008), and are shaped by a multitude of

environmental forces. As a result, association models between genotypes

and phenotypes are an important first step, but they are not the end of

the story. It is also important to investigate the extent to which genetic

associations are moderated by environmental factors and other genes.

4.2. SLC6A4, Serotonin, and Happiness

One strategy in behavioral genetics is to start with a “candidate” gene

that is thought to influence behaviors or processes in the body that are

related to the phenotype of interest. For subjective well-being, this means


14

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

focusing on genes that affect brain development, neurotransmitter synthesis

and reception, hormone regulation, and transcriptional factors (Damberg,

Garpenstrand, Hallman, and Oreland, 2001; Benjamin, Chabris, Glaeser,

Gudnason, Harris, Laibson, Launer, and Purcell, 2007).

We choose a candidate gene that has already received a great deal of at-

tention for its association with mental states. The SLC6A4 gene is critical

to the metabolism of serotonin in the brain. As shown in Figure 1, serotonin

is a chemical that is released by a neuron and sensed by a receptor on the

receiving neuron, passing an electric potential across a gap called a nerve

synapse (the nerve that emits the serotonin is on the “pre-synaptic” side of

the gap). Signals are carried throughout the body by the sequential release

of a neurotransmitter by one neuron after another across these synapses.

The SLC6A4 gene codes for the serotonin transporters (5-HTT or SERT)

that are placed in the cell wall and reabsorb the neurotransmitter sero-

tonin from the synaptic cleft. Most serotonin is recycled after use and the

serotonin transporter allows serotonergic neurons to restock. The serotonin

transporter gene has been studied extensively and much is known about the

way different versions of this gene influence serotonergic neurotransmission

which, in turn, is found to influence personality and mental health (Hariri,

Mattay, Tessitore, Kolachana, Fera, and Goldman, 2002; Hariri and Holmes,

2006; Canli and Lesch, 2007).

The SLC6A4 gene contains a 44 base-pair variable-number tandem repeat

(VNTR) polymorphism5 in the promoter region6(5-HTTLPR) that is be-

lieved to be responsible for variation in transcriptional efficiency. The “long”

(528 bp) and “short” (484 bp) polymorphism produce the same protein, but

5A VNTR polymorphism is a repeated segment of DNA that varies among individualsin a population.

6A promoter region is the regulatory region of DNA that tells transcription enzymeswhere to begin. These promoter regions typically lie upstream from the genes they con-trol.


15

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

Figure 1.— Representation of the long/short variant of the SLC6A4gene and the release, reception, and recycling of serotonin in neurons.Adapted from Canli & Lesch (2007), with permission from the Nature Pub-lishing Group.

the long allele is associated with an approximately three times higher basal

activity than the shorter allele. Consequently, the long variant produces

significantly more 5-HTT mRNA7 and protein (Lesch, Bengel, Heils, Sabol,

Greenberg, Petri, and et al., 1996; Little, McLaughlin, Zhang, Livermore,

Dalack, and McFinton, 1998; Glatz, Mossner, Heils, and Lesch, 2003; Canli

and Lesch, 2007). The long polymorphism thus results in increased gene

expression and more serotonin transporters in the cell membrane. In turn,

more serotonin is reintroduced into the pre-synaptic cell. This process is

also shown in Figure 1.

7Messenger ribonucleic acid (mRNA) is a type of RNA that carries information fromDNA to ribosomes. In turn, these ribosomes “read” messenger RNAs and translate theirinformation into proteins.


16

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

Functional variation in the serotonin transporter gene is increasingly un-

derstood to exert influence on parts of the brain regulated by serotoner-

gic neurotransmission. In particular, research shows increased amygdala

activation to negative emotional stimuli among carriers of short alleles

(Hariri, Mattay, Tessitore, Kolachana, Fera, and Goldman, 2002; Heinz,

Braus, Smolka, Wrase, Puls, Hermann, and et al., 2005; Munafo, Brown, and

Hariri, 2008; Pezawas, Meyer-Lindenberg, Drabant, Verchinski, Munoz, Ko-

lachana, Egan, Mattay, Hariri, and Weinberger, 2005; Canli, Omura, Haas,

Fallgatter, and Constable, 2005). A morphometrical study of this genetic as-

sociation reports reduced gray matter volume in short-allele carriers in lim-

bic regions critical for processing of negative emotion, particularly perigen-

ual cingulate and amygdala (Pezawas, Meyer-Lindenberg, Drabant, Verchin-

ski, Munoz, Kolachana, Egan, Mattay, Hariri, and Weinberger, 2005). These

authors conclude that 5-HTTLPR induced variation in anatomy and func-

tion of an amygdala-cingulate feedback circuit critical for emotion regulation

indicates one mechanism for a genetic susceptibility for depression (Pezawas,

Meyer-Lindenberg, Drabant, Verchinski, Munoz, Kolachana, Egan, Mattay,

Hariri, and Weinberger, 2005). Another morphometrical study corroborates

the finding that short-allele carriers show decreased volume in the affective

division of the anterior cingulate and decreased gray matter density in its

pregenual region (Canli, Omura, Haas, Fallgatter, and Constable, 2005).

The same study also finds that the 5-HTTLPR polymorphism is associated

with activation changes to positive stimuli, suggesting a general role in emo-

tional regulation, rather than negative valence specifically (Canli, Omura,

Haas, Fallgatter, and Constable, 2005).

Myriad behavioral studies also suggest that serotonin and SLC6A4 play

an important role in emotional regulation (Heils, Teufel, Petri, Stober,

Riederer, Bengel, and Lesch, 1996; Hariri, Mattay, Tessitore, Kolachana,

Fera, and Goldman, 2002; Hariri and Holmes, 2006). Specifically, variance


17

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

in 5-HTTLPR was found to be be associated with variation in mental health

outcomes (Lesch, Bengel, Heils, Sabol, Greenberg, Petri, and et al., 1996)

and subsequent studies report that about 10% of the variance in anxiety-

related traits depends on variation in serotonin transporters (Sen, Burmeis-

ter, and Ghosh, 2004; Munafo, Clark, and Flint, 2005). A recent study by

Fox, Ridgewell, and Ashwin (2009) also suggests that 5-HTTLPR may in-

fluence optimism. The authors obtained DNA from about 100 participants

and compared reaction times to pictures with positive, negative, and neu-

tral emotional valence (replicating a common experiment in psychopathol-

ogy research). The results show that individuals with the transcriptionally

more efficient 5-HTTLPR alleles display a significant bias towards process-

ing positive information and selectively avoiding negative information. This

emotionally self-protective pattern does not obtain in individuals carrying

one or both short alleles.

Not all studies show a direct relationship between a gene variant and a

phenotype. Instead, developmental or concurrent environments may moder-

ate an association between genes and phenotypes. A study by Caspi, Sugden,

Moffitt, Taylor, Craig, Harrington, McClay, Mill, Martin, Braithwaite, and

Poulton (2003) suggests a gene-environment interaction for the influence

of life stress on depression. The authors find that individuals with short

5-HTTLPR alleles gene are more vulnerable to stress-induced depression.

Among those individuals that had experienced a relatively large number

of stressful life events, about 33% of the carriers of the less efficient short

allele were cases of diagnosed depression as compared to only 17% of the in-

dividuals that carried both long alleles. Thus, in the Caspi, Sugden, Moffitt,

Taylor, Craig, Harrington, McClay, Mill, Martin, Braithwaite, and Poulton

(2003) study, the gene itself is not associated with depression. Rather, it

is the combination of both gene and environment that yields a significant

association. In this study we do not report on a gene-environment inter-


18

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

action, but the direct association between the number of long 5-HTTLPR

alleles and life satisfaction. Future research may produce new insights from

exploring how environmental factors moderate the association between 5-

HTTLPR and happiness.

4.3. Association methods

Genetic association studies test whether an allele or genotype occurs more

frequently within a group exhibiting a particular phenotype than those with-

out the phenotype. However, a significant association can mean one of three

things: (1) The allele itself influences subjective well-being; (2) the allele is

in “linkage disequilibrium” with an allele at another locus that influences

subjective well-being; or (3) the observed association is a false positive signal

due to population stratification.8

Population stratification occurs because groups may have different al-

lele frequencies due to their genetic ancestry. Subjective well-being in these

groups may be the product of their environments, alleles other than the one

of interest, or some unobserved reason. For example, two groups may not

have mixed in the past for cultural reasons. Through the process of local

adaptation or genetic drift, these groups may develop different frequencies

of a particular allele. At the same time, the two groups may also develop

divergent behaviors that are not influenced by the allele but solely by the

environment in which they live. Once these two groups mix in a larger

population, simply comparing the frequency of the allele to the observed

behavior would lead to a spurious association.

There are two main research designs employed in association studies, case-

control designs and family-based designs. Case-control designs compare the

8Given our data, we cannot differentiate between 1 and 2. In order to do so, we wouldneed additional genetic information about loci in close proximity to the locus of interest.Thus, a significant association means that either a particular allele, or one likely near iton the same gene, significantly influences subjective well-being.


19

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

frequency of alleles or genotypes among subjects that exhibit a phenotype

of interest to subjects who do not. As a result, case-control designs are

vulnerable to population stratification if either group is especially prone

to selection effects. A typical way to control for this problem is to include

controls for the race or ethnicity of the subject or to limit the analysis to a

specific racial or ethnic group. Family-based designs eliminate the problem

of population stratification by using family members, such as parents or

siblings, as controls. Tests using family data compare whether offspring

exhibiting the trait receive a risk allele from their parents more often than

would be expected by chance. This design is very powerful in minimizing

type I error but also suffers from much lower power in detecting a true

association. Xu and Shete (2006) show, based on extensive simulation work,

that a case-control association study using mixed-effects regression analysis

outperforms family-based designs in detecting an association while at the

same time effectively limiting type I error.

Hence, to test for genetic association we employ a mixed-effects OLS

regression model:9

Yij = β0 + βGGij + βkZkij + Uj + εij

where i and j index subject and family respectively. For the SLC6A4 gene,

G = 2 if the subject’s genotype is LL, G = 1 for genotypes LS or SL,

and G = 0 if the subject’s genotype is SS (where L represents having a

9The choice between OLS and ordered probit regression analysis rests on whether thecategories of the life satisfaction are considered cardinal or ordinal. Economists typicallyconsider these happiness scores as ordinal and have mainly opted for the ordered typeof analysis. Psychologists and sociologists interpret happiness categories as cardinal andtherefore use OLS. Ferrer-i-Carbonell and Frijters (2004) survey and test both empiricalliteratures to conclude that assuming cardinality or ordinality of happiness surveys makeslittle difference in studies where the dependent variable is measured at a single point intime. We opted for OLS, but other analyses using ordered probit reveal no meaningfuldifferences in coefficients or significance.


20

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

copy of a 528 base-pair “long” allele, and S represents having a copy of a

484 base-pair “short” allele). Z is a matrix of variables to control for the

underlying population structure of the Add Health sample as well as poten-

tially mediating factors such as age, gender, education, religiosity, marriage,

job, welfare, or medication that may all influence subjective well-being. Fi-

nally, the variable U is a family random effect that controls for potential

genetic and environmental correlation among family members, and ε is an

individual-specific error.

To control for the effects of the underlying population structure, we in-

clude indicator variables for whether a subject self-reported as Black, His-

panic, or Asian (base category is White). Following the policy of the United

States Census, Add Health allows respondents to mark more than one race.

Since this complicates the ability to control for stratification, we exclude

these individuals (N = 117), but a supplementary analysis including them

yields substantively equal results.

4.4. Association results

Table II shows the results of several specifications of the models to test

the hypothesis that the 5-HTTLPR long allele is associated with subjective

well-being. Each of these specifications includes variables for age, gender,

and race to control for population stratification. Model 1 shows that the long

allele is significantly associated with increased life satisfaction (p = 0.012).

In Figure 2, we summarize the results for 5-HTTLPR by simulating first

differences from the coefficient covariance matrix of Model 1. Holding all else

constant and changing the 5-HTTLPR variant for all subjects from zero to

one long allele would increase the reporting of being very satisfied with one’s

life in this population by about 8.5%. Similarly, changing the 5-HTTLPR

variant from zero to two long alleles would increase the reporting of being

very satisified by about 17.3%.


21

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

TABLE II

OLS models of association between 5-HTTLPR and lifesatisfaction.

Model 1 Model 2 Model 3Coeff. SE P-value Coeff. SE P-value Coeff. SE P-value

5-HTTLPR long 0.059 0.023 0.012 0.065 0.023 0.005 0.070 0.029 0.017Black -0.111 0.048 0.021 -0.114 0.049 0.020Hispanic 0.198 0.117 0.092 0.216 0.118 0.067Asian -0.196 0.073 0.007 -0.221 0.071 0.002Age 0.004 0.009 0.705 -0.011 0.009 0.262 -0.031 0.012 0.008Male 0.014 0.033 0.682 0.028 0.033 0.406 0.039 0.041 0.341Job 0.093 0.041 0.024 0.104 0.057 0.071College 0.115 0.033 0.001 0.238 0.042 0.000Married 0.232 0.041 0.000 0.318 0.050 0.000Divorced -0.313 0.153 0.041 -0.310 0.155 0.047Religiosity 0.103 0.017 0.000 0.082 0.023 0.000Welfare -0.236 0.098 0.017 -0.121 0.153 0.432Medication -0.045 0.032 0.162 -0.095 0.041 0.021Intercept 4.078 0.208 0.000 4.096 0.210 0.000 4.514 0.262 0.000N 2545 2528 1446R2 0.01 0.06 0.08

Note: Variable definitions are in the Appendix. Standard errors (SE) and P-valuesare also presented.

Model 2 includes a number of socio-economic factors that are known

to influence subjective well-being. In particular, having a job, education,

marriage, divorce, religiosity, welfare assistance, and being on medication.

This model also suggests that there is a statistically significant association

(p = 0.005) between the 5-HTTLPR long variant and the reporting of

life satisfaction. Notice also that the coefficient actually increases a bit,

suggesting that the association cannot be explained by a mediation effect

this genotype may have on any other variables included in the model.10

10We also report the results of association tests with 5-HTTLPR for each of these socio-economic factors in the appendix. An association with medication is nearly significant(p = 0.08) but loses its significance (p = 0.17) when controlling for age, gender, and race.Hence, medication cannot be considered a mediating variable (Baron and Kenny, 1986).


22

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

One Two Number of long 5-HTTLPR alleles

Cha

nge

in L

ikel

ihoo

d of

Bei

ng V

ery

Sat

isfie

d W

ith Y

our L

ife (%

)

05

1015

2025

3035

Figure 2.— Increasing the number of “long,” more efficient 5-HTTLPR al-leles yields significantly higher life satisfaction. First differences, based on simula-tions of Model 1 parameters, are presented along with 95% confidence intervals.All other variables are held at their means.

Following Xu and Shete (2006), as a robustness test for population strat-

ification, we also include Model 3 that is a case-control association model

for those subjects that uniquely identified themselves as being white. The

coefficient on 5-HTTLPR and its p-value (p = 0.017) suggest that popula-

tion stratification between self-reported racial categories is not driving the

association between 5-HTTLPR and life satisfaction.

5. REPLICATION: THE FRAMINGHAM HEART STUDY

Specific genotypes usually only account for a very small amount of the

variance in complex social behaviors, which means the tests often have low

power. As a result, it is very important to replicate results in independent

samples. Here, we utilize the Framingham Heart Study (FHS), a population-

based, longitudinal, observational cohort study that was initiated in 1948

to prospectively investigate risk factors for cardiovascular disease. Since

then, the FHS has come to be composed of four separate but related cohort

populations: (1) the Original Cohort enrolled in 1948 (N=5,209); (2) the


23

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

Offspring Cohort (the children of the Original Cohort and spouses of the

children) enrolled in 1971 (N=5,124); (3) the Omni Cohort enrolled in 1994

(N=508); and (4) the Generation 3 Cohort (the grandchildren of the Orig-

inal Cohort) enrolled beginning in 2002 (N=4,095). Published reports pro-

vide details about sample composition and study design for all these cohorts

(Cupples and D’Agnostino, 1988; Kannel, Feinleib, McNamara, Garrison,

and Castelli, 1979).

The Framingham Heart Study makes available genetic markers for its

participants. Out of the 14,428 members of the three main cohorts, a total

of 9,237 individuals have been genotyped (4,986 women and 4,251 men)

for single nucleotide polymorphisms (SNPs). These are specific locations

on human DNA where a single pair of nucleotides varies for some part of

the human population. FHS makes available a data set of expected geno-

types for all 2,543,887 SNPs in the European ancestry HapMap sample that

was computed from the 550,000 observed SNPs from an Affymetrix array

using the program MACH (for information on how this data set was con-

structed, see De Bakker (2008)). Although this data does not contain the

same VNTR polymorphism marker for SLC6A4 that we analyze in Add

Health, it does contain a nearby marker called “rs2020933”, and the “A”

allele of this marker is known to be associated with higher transcriptional

efficiency of serotonin transporters (Martin, Cleak, Willis-Owen, Flint, and

Shifman, 2007; Wendland, Martin, Kruse, Lesch, and Murphy, 2006; Lip-

sky, Hu, and Goldman, 2009; Fahad, Vasiliou, Haddley, Paredes, Roberts,

Miyajima, Klenova, Bubb, and Quinn, 2010). It is also known to be in pos-

itive linkage disequilibrium with the long allele of 5-HTTLPR (Huezo-Diaz,

Rietschel, Henigsberg, Marusic, Mors, Maier, Hauser, Souery, Placentino,

Zobel, Larsen, Czerski, Gupta, Hoda, Perroud, Farmer, Craig, Aitchison,

and McGuffin, 2009). The FHS also asked 3,460 participants in the off-

spring cohort a variant of the life satisfaction question: “Indicate where you


24

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

think you belong between these two extremes ... satisfied with job or home

life OR ambitious, want change.” Respondents were given a 7 point scale to

choose from, and we reverse coded the scale so that higher values indicated

greater satisfaction with life (mean=4.7, SD=1.7). Although this question

is not exactly like the one asked in Add Health, if there is a real associa-

tion between SLC6A4 and happiness, we expect it to show up in spite of

variations in the way the question is asked.

We merged the gene and life satisfaction data and conducted an asso-

ciation test using a linear regression with a general estimating equations

(GEE) approach to account for within-family correlation of errors. As shown

in Model 1 in Table III, this association is significant (p = 0.05) and in the

expected direction. In Model 2 we include additional controls for age and

gender. We also include the first ten principal components of a singular

value decomposition of the subject-genotype matrix in the regression (see

Appendix), which has been shown to effectively control for population strat-

ification (Price, Patterson, Plenge, Weinblatt, Shadick, and Reich, 2006).

Once again, the replicated association is significant (p = 0.05).

6. DISCUSSION

Our main objective here has been to provide empirical evidence that genes

matter for subjective well-being and to encourage economists to consider

the importance of biological differences. The results we present address one

possible source of the “baseline” or “set point” for happiness that prior work

has identified (Kahneman, Diener, and Schwarz, 1999; Graham, 2008). The

existence of a baseline does not mean that the socio-economic influences

on happiness so far identified by researchers are unimportant. Rather, our

results complement these studies and suggest a new direction for research.

As indicated by the R2 value in Table II, the SLC6A4 gene explains less than

one percent of the variation in life satisfaction, but our twin analysis suggests


25

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

TABLE III

GEE models of association between rs2020933 and life satisfaction.

Model 1 Model 2Coeff. SE P-value Coeff. SE P-value

rs2020933 “A” alleles 0.22 0.11 0.05 0.21 0.11 0.05Age 0.04 0.00 0.00Male -0.00 0.06 0.99Principal Component 1 -0.88 1.57 0.58Principal Component 2 0.04 6.43 0.99Principal Component 3 -3.32 2.21 0.13Principal Component 4 -1.08 2.33 0.64Principal Component 5 -3.30 2.64 0.21Principal Component 6 1.13 2.45 0.65Principal Component 7 2.21 1.97 0.26Principal Component 8 -2.10 2.21 0.34Principal Component 9 -0.52 2.06 0.80Principal Component 10 -1.82 2.26 0.42Intercept 4.68 0.04 0.00 2.90 0.16 0.00N 2843 2831R2 0.01 0.05

Note: Variable definitions are in the Appendix. Standard errors (SE) and P-values are also presented.

that all genes together account for about a third of the total variance.

Therefore, there are probably many other genes which, in conjunction with

environmental factors, help to explain how baseline happiness varies from

one person to another. The association with SLC6A4 is probably the first

of a number of associations that will likely be identified over the course of

the next few years.

Another use of work such as this is to address the problem of omitted

variable bias (OVB). A missing variable might be linked to multiple param-

eters and thus bias the estimate of the causal effect of X on Y. To the extent

that genetic attributes are a source of OVB, and to the extent that they

can be added to models of economic outcomes and behaviors, accounting

for such variables will improve causal estimates of other attributes.


26

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

While the Add Health study presents us with a valuable opportunity to

explore a genetic basis of subjective well-being, we want to emphasize a

limitation of the data. The Add Health sample is restricted to individuals

who are 18-26 years old during Wave III, so our results apply only to the

subjective well-being of young adults and not to people in different age cat-

egories. However, the strong similarity in the distribution of answers in the

Add Health data as compared to other life satisfaction surveys used in the

happiness literature suggests that the age limits are not likely to gravely

distort our results (Di Tella, MacCulloch, and Oswald, 2001, 2003; Kahne-

man and Krueger, 2006; Frey, 2008). Moreover, our successful replication in

the Framingham Heart Study, which has a much wider age range, further

suggests a degree of generalizability.

A second important limitation is that we use a case-control method that

is vulnerable to population stratification. Because of limited mobility, local

adaptation, and genetic drift, it is possible that people from different cul-

tures have a different incidence of certain genotypes, which could lead to a

spurious association between genotype and cultural attributes. We limit this

potential threat to the validity of our results by including controls for race

and limiting the analysis to a specific racial or ethnic group in Add Health.

Moreover, we successfully replicate a related association in the Framing-

ham Heart Study that controls for the first ten principal components of

a singular value decomposition of the subject-genotype matrix, which has

been shown to effectively deal with the problem of population stratification

(Price, Patterson, Plenge, Weinblatt, Shadick, and Reich, 2006).

The estimates of the influence of socio-demographic, economic, and cul-

tural covariates on life satisfaction in Table II corroborate the generally

identified systematic effects of these variables in the literature (for a sur-

vey, see Dolan, Peasgood, and White 2008). In particular, gender does not

systematically affect happiness. Higher age has a negative, though not sta-


27

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

tistically significant effect (this is not surprising considering that our sample

refers to young adults). African Americans and Asian Americans are system-

atically less happy than are Whites, while Latinos are somewhat happier,

but not in a statistically significant way. Better educated and married in-

dividuals report having significantly higher life satisfaction, while divorced

people are more unhappy. Having a job strongly raises life satisfaction. This

reflects the psychic benefits of being occupied and integrated into society.

At the same time it suggests that having an income raises life satisfaction.

In contrast, persons on welfare are much less happy than those employed

which reflects the psychic costs of unemployment. Religious individuals are

significantly more happy than those without religious beliefs. Persons with

less good health, as measured by the need to be on medication, are also

less happy. As is the case with most research on happiness, these estimates

identify correlations, not causality, given the difficulty in disentangling endo-

geneity. Once again, consistency with previous studies suggests that results

using the Add Health data may generalize to other populations and a wider

demography in terms of age.

The life satisfaction question and answer formulation used in Add Health

is standard in the economics and psychology literatures (Diener and Diener,

1996; Di Tella, MacCulloch, and Oswald, 2001; Kahneman and Krueger,

2006; Frey, 2008). This question has been cross-validated with alternative

measures that gauge subjective well-being (Kahneman and Krueger, 2006;

Bartels and Boomsma, 2009) and Oswald and Wu (2010) provide objective

confirmation of life satisfaction as a measure of subjective well-being. Still,

the life satisfaction question has been criticized for inducing a focussing illu-

sion by drawing attention to people’s relative standing rather than moment-

to-moment hedonic experience (Kahneman, Krueger, Schkade, Schwarz, and

Stone, 2006).


28

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

7. CONCLUSION

Our results suggest that genetic factors significantly influence individual

subjective well-being. Using twin study techniques we estimate that genetics

explains about 33% of the variance in individual happiness. Moreover, using

alternative methods we have identified one particular gene—SLC6A4 —as

having a positive association with self-reported life satisfaction in two inde-

pendent samples. By moving beyond a twin study and focusing on specific

genes, our analysis is able to suggest potential causal pathways through

which genes influence happiness levels. A significant body of research has

shown that the serotonin transporter gene influences the human psyche via

its impact on neurological processes, thereby establishing a potential causal

chain leading from this genotype to self-reported life satisfaction. Given

prior research linking the “short,” less trancriptionally efficient, alleles of

the SLC6A4 gene to mood disorders, and the “long,” more efficient alleles

to optimism bias, we hypothesized that carriers of the “long” alleles would

be more likely to report being happy, and this intuition is supported in both

the Add Health and Framingham Heart Study data. The causal structure

must be further studied once additional data reporting the genetic endow-

ment of individuals coupled with data on their subjective well-being become

available.

We have stressed that genetic factors complement, rather than substi-

tute for, the existing studies showing the influence of socio-demographic,

economic and cultural variables on life satisfaction. Future work could at-

tempt to identify other genes or gene-environment interactions that are

implicated in subjective well-being. Finding out which genes they are and

what physical function they have will improve our understanding of the bi-

ological processes that underlie economic outcomes like well-being and may

also shed light on their evolutionary origin (Fitzpatrick, Ben-Shahar, Smid,

Vet, Robinson, and Sokolowski, 2005). While the SLC6A4 gene may ex-


29

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

plain a significant portion of the variation in happiness, it is important to

re-emphasize that there is no single “happiness gene.” Instead, there is likely

to be a set of genes whose expression, in combination with environmental

factors, influences subjective well-being.

More broadly, these results suggest that integrating the unique biology

of each individual, in addition to studying experience and environment,

may usefully complement existing models and increase their explanatory

power (Caplin and Dean, 2008). We also believe that genetic association

studies such as ours may be a new catalyst for two important lines of re-

search. First, economics places a high premium on causal inference. Provided

that robust genetic associations are available and that exclusion restrictions

are met, genotypes could function as instrumental variables to disentangle

the reverse causality in important relationships that have been plagued

by endogeneity. First attempts at using genes as instruments have been

tried on the link between health and educational attainment (Fletcher and

Lehrer, 2009; Norton and Han, 2009; von Hinke Kessler Scholder, Smith,

Lawlor, Propper, and Windmeijer, 2010; Beauchamp, Cesarini, Johannes-

son, van der Loos, Koellinger, Broenen, Fowler, Rosenquist, Thurik, and

Christakis, 2010; O’Malley, Rosenquist, Zaslavsky, and Christakis, 2010).

We foresee this to be a promising avenue in economic research. Second, in-

tegrating genetic variation and neuroscientific research may further advance

our understanding of the biological underpinnings of individual behavior.

For example, the work by Urry, Nitschke, Dolski, Jackson, Dalton, Mueller,

Rosenkranz, Ryff, Singer, and Davidson (2004) presents neural correlates of

subjective well-being. Some of the neurological variation they observe may

result from differences in genotype and could thus inform and stimulate new

candidate gene association studies. Since genes are upstream from neuro-

logical processes, understanding them may bring us closer to understanding

the objective sources of subjective well-being.


30

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

ACKNOWLEDGEMENTS

This research uses data from Add Health, a program project directed by

Kathleen Mullan Harris at the University of North Carolina at Chapel Hill,

and funded by grant P01-HD31921 from the Eunice Kennedy Shriver Na-

tional Institute of Child Health and Human Development, with cooperative

funding from 23 other federal agencies and foundations. No direct support

was received from grant P01-HD31921 for this analysis.

REFERENCES

Add Health Biomarker Team (2007): “Biomarkers in Wave III of the Add Health

Study,” http://www.cpc.unc.edu/projects/addhealth/files/biomark.pdf.

Alcott, H., D. Karlan, M. Mobius, T. Rosenblat, and A. Szeidl (2007): “Com-

munity Size and Network Closure,” American Economic Review, 97(2), 80–85.

Ashenfelter, O., and A. B. Krueger (1994): “Estimates of the Economic Return to

Schooling from a New Sample of Twins,” American Economic Review, 84(5), 1157–73.

Baron, R., and D. Kenny (1986): “The moderator-mediator variable distinction in

social psychological research: Conceptual, strategic, and statistical considerations,”

Journal of Personality and Social Psychology, 51(6), 1173–1182.

Bartels, M., and D. I. Boomsma (2009): “Born to be Happy? The Etiology of

Subjective Well-Being,” Behavior Genetics, 39(6), 605–615.

Beauchamp, J., D. Cesarini, M. Johannesson, M. van der Loos,

P. Koellinger, P. Broenen, J. Fowler, J. Rosenquist, A. Thurik, and

N. Christakis (2010): “Molecular Genetics and Economics,” Mimeo: Harvard Uni-

versity.

Benjamin, D. J., C. F. Chabris, E. L. Glaeser, V. Gudnason, T. B. Harris,

D. I. Laibson, L. Launer, and S. Purcell (2007): “Genoeconomics,” in Bioso-

cial Surveys, ed. by M. Weinstein, J. W. Vaupel, and K. W. Wachter. The National

Academies Press: Washington, D.C.

Bertolino, A., G. Arciero, V. Rubino, V. Latorre, M. D. Candia, and

V. Mazzola (2005): “Variation of human amygdala response during threatening stim-

uli as a function of 5HTTLPR genotype and personality style,” Biological Psychiatry,

57(12), 1517–1525.


31

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

Bouchard, T. (1998): “Genetic and Environmental Influences on Adult Intelligence

and Special Mental Abilities,” Human Biology, 70(2), 257–279.

Canli, T., and K. Lesch (2007): “Long story short: the serotonin transporter in

emotion regulation and social cognition,” Nature Neuroscience, 10(9), 1103–1109.

Canli, T., K. Omura, B. W. Haas, A. Fallgatter, and R. T. Constable (2005):

“Beyond affect: a role for genetic variation of the serotonin transporter in neural

activation during a cognitive attention task,” Proceedings of the National Academy of

Sciences USA, 102, 12224–12229.

Caplin, A., and M. Dean (2008): “Dopamine, Reward Prediction Error, and Eco-

nomics,” Quarterly Journal of Economics, 123(2), 663–701.

Caspi, A., K. Sugden, T. Moffitt, A. Taylor, I. Craig, H. Harrington, J. Mc-

Clay, J. Mill, J. Martin, A. Braithwaite, and R. Poulton (2003): “Influence

of Life Stress on Depression: Moderation by a Polymorphism in the 5-HTT Gene,”

Science, 301, 386–389.

Cesarini, D., C. T. Dawes, M. Johannesson, P. Lichtenstein, and B. Wal-

lace (2009): “Genetic Variation in Preferences for Giving and Risk-Taking,” Quar-

terly Journal of Economics, 124(2), 809–842.

Clark, A. E., P. Frijters, and M. A. Shields (2008): “Relative Income, Happiness,

and Utility: An Explanation for the Easterlin Paradox and Other Puzzles,” Journal

of Economic Literature, 46(1), 95–144.

Clark, A. E., and A. J. Oswald (1996): “Satisfaction and Comparison Income,”

Journal of Public Economics, 61(3), 359–381.

Cupples, L., and R. D’Agnostino (1988): “Survival following initial cardiovascular

events: 30 year follow-up,” in The Framingham Study: An epidemiological investigation

of cardiovascular disease, ed. by W. B. Kannel, P. A. Wolf, and R. J. Garrison, pp.

88–2969. National Heart, Lung and Blood Institute, Bethesda, MD.

Damberg, M., H. Garpenstrand, J. Hallman, and L. Oreland (2001): “Genetic

mechanisms of behavior: don’t forget about the transcription factors,” Molecular Psy-

chiatry, 6(5), 503–510.

De Bakker, P. (2008): “Imputation in the Framingham Heart Study,” Mimeo: Harvard

Medical School.

Di Tella, R., and R. MacCulloch (2006): “Some Uses of Happiness Data in Eco-

nomics,” Journal of Economic Perspectives, 20, 25–46.

Di Tella, R., R. MacCulloch, and A. J. Oswald (2001): “Preferences over Infla-

tion and Unemployment: Evidence from Surveys of Happiness,” American Economic


32

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

Review, 91(1), 335–341.

(2003): “The Macroeconomics of Happiness,” Review of Economics and Statis-

tics, 85(4), 809–827.

Diener, E., and C. Diener (1996): “Most People Are Happy,” Psychological Science,

7(3), 181–185.

Diener, E., and R. Lucas (1999): “Personality and subjective well-being,” in Well-

being: The foundations of hedonic psychology, ed. by D. Kahneman, E. Diener, and

N. Schwarz. Sage, New York, NY.

Dolan, P., T. Peasgood, and M. White (2008): “Do we really know what makes us

happy? A review of the economic literature on the factors associated with subjective

well-being,” Journal of Economic Psychology, 29(94-122).

Easterlin, R. (1974): “Does Economic Growth Improve the Human Lot? Some Em-

pirical Evidence,” in Nations and Households in Economic Growth: Essays in Honour

of Moses Abramowitz, ed. by P. David, and M. Reder. Academic Press.

Echenique, F., and R. G. Fryer (2007): “A Measure of Segregation Based on Social

Interactions,” Quarterly Journal of Economics, 122(2), 441–485.

Echenique, F., R. G. Fryer, and A. Kaufman (2006): “Is School Segregation Good

or Bad?,” American Economic Review, 96(2), 265–269.

Fahad, A. R., S. A. Vasiliou, K. Haddley, U. M. Paredes, J. C. Roberts,

F. Miyajima, E. Klenova, V. J. Bubb, and J. Quinn (2010): “Combinatorial

interaction between two human serotonin transporter gene variable number tandem

repeats and their regulation by CTCF,” Journal of Neurochemistry, 112(1), 296–306.

Ferrer-i-Carbonell, A., and P. Frijters (2004): “How Important is Methodol-

ogy for the Estimates of the Determinants of Happiness,” The Economic Journal,

114(July), 641–659.

Fitzpatrick, M., Y. Ben-Shahar, H. Smid, L. Vet, G. Robinson, and

M. Sokolowski (2005): “Candidate genes for behavioural ecology,” Trends in Ecology

and Evolution, 20(2), 96–104.

Fletcher, J., and S. Lehrer (2009): “Using Genetic Lotteries within Families to

Examine the Causal Impact of Poor Health on Academic Achievement,” Prepared for

the Annual Meeting of the American Economic Association.

Fowler, J. H., L. A. Baker, and C. T. Dawes (2008): “Genetic Variation in Political

Participation,” American Political Science Review, 101(2), 233–248.

Fowler, J. H., and N. A. Christakis (2008): “Dynamic Spread of Happiness in a

Large Social Network: Longitudinal Analysis Over 20 Years in the Framingham Heart


33

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

Study,” British Medical Journal, 337(2338), 1–9.

Fowler, J. H., C. T. Dawes, and N. A. Christakis (2009): “Model of Genetic Vari-

ation in Human Social Networks,” Proceedings of the National Academy of Sciences

USA, 106(6), 1720–1724.

Fox, E., A. Ridgewell, and C. Ashwin (2009): “Looking on the bright side: biased

attention and the human serotonin transporter gene,” Proceedings of the Royal Society

B, 276, 1747–1751.

Frey, B. S. (2008): Happiness: A Revolution in Economics. MIT Press.

Frey, B. S., and A. Stutzer (2002a): Happiness and Economics: How the Economy

and Institutions Affect Well-Being. Princeton University Press.

(2002b): “What Can Economists Learn from Happiness Research?,” Journal of

Economic Literature, 40(2), 402–435.

Glatz, K., R. Mossner, A. Heils, and K. Lesch (2003): “Glucocorticoid-regulated

human serotonin transporter (5-HTT) expression is modulated by the 5-HTT gene-

promotor-linked polymorphic region,” J. Neurochem., 85(5), 1072–1078.

Graham, C. (2008): “Economics of Happiness,” in The New Palgrave Dictionary of

Economics, ed. by S. N. Durlauf, and L. E. Blume. Palgrave Macmillan.

Graham, C., and S. Pettinato (2002): Happiness and Hardship: Opportunity and

Insecurity in New Market Economics. Brookings Institution Press.

Hariri, A., and A. Holmes (2006): “Genetics of emotional regulation: the role of the

serotonin transporter in neural function,” Trends Cognitive Science, 10, 182–191.

Hariri, A., V. Mattay, A. Tessitore, B. Kolachana, F. Fera, and D. Gold-

man (2002): “Serotonin transporter genetic variation and the response of the human

amygdala,” Science, 297(5580), 400–403.

Harris, K. M., C. T. Halpern, A. Smolen, and B. C. Haberstick (2006): “The

National Longitudinal Study of Adolescent Health (Add Health) Twin Data,” Twin

Research and Human Genetics, 9(6), 988–997.

Heils, A., A. Teufel, S. Petri, G. Stober, P. Riederer, D. Bengel, and

K. Lesch (1996): “Allelic Variation of Human Serotonin Transporter Gene Expres-

sion,” J. Neurochem., 66(6), 2621–2624.

Heinz, A., D. Braus, M. Smolka, J. Wrase, I. Puls, D. Hermann, and et al.

(2005): “Amygdala-prefrontal coupling depends on a genetic variation of the serotonin

transporter,” Nat Neurosci, 8(1), 20–21.

Huezo-Diaz, P., M. Rietschel, N. Henigsberg, A. Marusic, O. Mors,

W. Maier, J. Hauser, D. Souery, A. Placentino, A. Zobel, E. R. Larsen,


34

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

P. M. Czerski, B. Gupta, F. Hoda, N. Perroud, A. Farmer, I. Craig, K. J.

Aitchison, and P. McGuffin (2009): “Moderation of antidepressant response by

the serotonin transporter gene,” British Journal of Psychiatry, 195, 30–38.

Jacobson, K., and D. Rowe (1998): “Genetic and Shared Environmental Influences

on Adolescent BMI: Interactions with Race and Sex,” Behavior Genetics, 28(4), 265–

278.

Kahneman, D., E. Diener, and N. Schwarz (1999): Well-being: The Foundations

of Hedonic Psychology. Russel Sage, New York.

Kahneman, D., and A. B. Krueger (2006): “Developments in the Measurement of

Subjective Well-Being,” Journal of Economic Perspectives, 20, 3–24.

Kahneman, D., A. B. Krueger, D. Schkade, N. Schwarz, and A. A. Stone

(2006): “Would You Be Happier If You Were Richer? A Focusing Illusion,” Science,

312(5782), 1908–1910.

Kannel, W., M. Feinleib, P. McNamara, R. Garrison, and W. Castelli (1979):

“An investigation of coronary heart disease in families,” American Journal of Epidemi-

ology, 110, 281–290.

Kendler, K. S., M. C. Neale, R. C. Kessler, A. C. Heath, and L. J. Eaves

(1993): “A Test of the Equal-Environment Assumption in Twin Studies of Psychiatric

Illness,” Behavior Genetics, 23, 21–27.

Layard, R. (2005): Happiness: Lessons from a New Science. Penguin.

Lesch, K., D. Bengel, A. Heils, S. Sabol, B. Greenberg, S. Petri, and et al.

(1996): “Association of anxiety-related traits with a polymorphism in the serotonin

transporter gene regulatory region,” Science, 274, 1527–1531.

Lipsky, R. H., X.-Z. Hu, and D. Goldman (2009): “Additional Functional Variation

at the SLC6A4 Gene,” American Journal of Medical Genetics B Neuropsychiatric

Genetics, 150B(1), 153.

Little, K., D. McLaughlin, L. Zhang, C. Livermore, G. Dalack, and

P. McFinton (1998): “Cocaine, ethanol, and genotype effects on human midbrain

serotonin transporter binding sites and mRNA levels,” American Journal of Psychia-

try, 155, 207–213.

Luttmer, E. F. (2005): “Neighbors As Negatives: Relative Earnings And Well-Being,”

Quarterly Journal of Economics, 120(3), 963–1002.

Lykken, D., and A. Tellegen (1996): “Happiness is a Stochastic Phenomenon,”

Psychological Science, 7(3), 186–189.

Mackay, T. (2001): “The genetic architecture of quantitative traits,” Annu Rev Genet,


35

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

35, 303–339.

Martin, J., J. Cleak, S. Willis-Owen, J. Flint, and S. Shifman (2007): “Map-

ping regulatory variants for the serotonin transporter gene based on allelic expression

imbalance,” Molecular Psychiatry, 12(5), 421–2.

Munafo, M. R., S. M. Brown, and A. R. Hariri (2008): “Serotonin transporter

(5HTTLPR) genotype and amygdala activation: a meta-analysis,” Biological Psychi-

atry, 63, 852–857.

Munafo, M. R., T. Clark, and J. Flint (2005): “Does measurement instrument

moderate the association between the serotonin transporter gene and anxiety-related

personality traits? A meta-analysis.,” Molecular Psychiatry, 10, 415–419.

Neale, M., S. Boker, G. Xie, and H. Maes (2010): “OpenMx - Advanced Structural

Equation Modeling (http://openmx.psyc.virginia.edu/),” .

Nes, R. B., E. Roysamb, K. Tambs, J. R. Harris, and T. Reichborn-Kjennerud

(2006): “Subjective well-being: genetic and environmental contributions to stability

and change,” Psychological medicine, 36(7), 1033–1042.

Norton, E., and E. Han (2009): “How Smoking, Drugs, and Obesity Affect Educa-

tion, Using Genes as Instruments,” Prepared for the Annual Meeting of the American

Economic Association.

O’Malley, A., J. Rosenquist, A. Zaslavsky, and N. Christakis (2010): “Esti-

mation of Peer Effects in Longitudinal Models Using Genetic Alleles as Instrumental

Variables,” Mimeo: Harvard University.

Oswald, A. J., and S. Wu (2010): “Objective Confirmation of Subjective Measures

of Human Well-Being: Evidence from the U.S.A,” Science, 327, 576–579.

Pezawas, L., A. Meyer-Lindenberg, E. M. Drabant, B. A. Verchinski, K. E.

Munoz, B. S. Kolachana, M. F. Egan, V. S. Mattay, A. R. Hariri, and D. R.

Weinberger (2005): “5-HTTLPR polymorphism impacts human cingulate-amygdala

interactions: a genetic susceptibility mechanism for depression,” Nature Neuroscience,

8, 828–834.

Plomin, R., J. C. DeFries, G. E. McClearn, and P. McGuffin (2008): Behavioral

genetics. Worth Publishers, New York, NY, 5th ed edn.

Price, A. L., N. J. Patterson, R. M. Plenge, M. E. Weinblatt, N. A. Shadick,

and D. Reich (2006): “Principal components analysis corrects for stratification in

genome-wide association studies,” Nature Genetics, 38, 904–909.

Rayo, L., and G. S. Becker (2007): “Habits, Peers, and Happiness: An Evolutionary

Perspective,” American Economic Review, 97(2), 487–491.


36

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

Scarr, S., and L. Carter-Saltzman (1979): “Twin Method: Defense of a Critical

Assumption,” Behavior Genetics, 9, 527–542.

Sen, S., M. L. Burmeister, and D. Ghosh (2004): “Meta-analysis of the association

between a serotonin transporter promoter polymorphism (5-HTTLPR) and anxiety

related personality traits,” Am. J. Med. Genet. B, 127, 85–89.

Stevenson, B., and J. Wolfers (2008): “Economic Growth and Subjective Well-

being: Reassessing the Easterlin Paradox,” Brookings Papers on Economic Activity,

2, 1–87.

Stubbe, J., D. Posthuma, D. Boomsma, and E. De Geus (2005): “Heritability of

life satisfaction in adults: a twin-family study,” Psychological Medicine, 35(11), 1581–8.

Taubman, P. (1976): “The determinants of earnings: Genetics, family, and other envi-

ronments: A study of white male twins.,” American Economic Review, 66, 858–870.

Urry, H. L., J. B. Nitschke, I. Dolski, D. C. Jackson, K. M. Dalton, C. J.

Mueller, M. A. Rosenkranz, C. D. Ryff, B. H. Singer, and R. J. Davidson

(2004): “Making a Life Worth Living: Neural Correlates of Well-Being,” Psychological

Science, 15(6), 367–372.

Van Praag, B., and A. Ferrer-I-Carbonell (2004): Happiness Quantified: A Sat-

isfaction Calculus Approach. Oxford University Press.

Visscher, P., S. Medland, M. Ferreira, K. Morley, G. Zhu, B. Cornes,

G. Montgomery, and N. Martin (2006): “Assumption-free estimation of heri-

tability from genome-wide identity-by-descent sharing between full siblings,” PLoS

Genetics, 2:e41.

von Hinke Kessler Scholder, S., G. D. Smith, D. A. Lawlor, C. Propper, and

F. Windmeijer (2010): “Genetic Markers as Instrumental Variables: An Application

to Child Fat Mass and Academic Achievement,” University of Bristol, Centre for

Market and Public Organisation Working Paper 10/229.

Wendland, J., B. Martin, M. Kruse, K. Lesch, and D. Murphy (2006): “Simul-

taneous genotyping of four functional loci of human SLC6A4, with a reappraisal of

5-HTTLPR and rs25531,” Molecular Psychiatry, 11(3), 224–6.

Xian, H., J. F. Scherrer, S. A. Eisen, W. R. True, A. C. Heath, J. Gold-

berg, M. J. Lyons, and M. T. Tsuang (2000): “Self-Reported Zygosity and the

Equal-Environments Assumption for Psychiatric Disorders in the Vietnam Era Twin

Registry,” Behavior Genetics, 30, 303–310.

Xu, H., and S. Shete (2006): “Mixed-effects Logistic Approach for Association Follow-

ing Linkage Scan for Complex Disorders,” Annals of Human Genetics, 71(2), 230–237.


37

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

APPENDIX A

Variable Definitions

5-HTTLPR long is an variable for having 0, 1, or 2 of the 528 base-pair

alleles of the SLC6A4 gene (as opposed to the 484 base-pair version). The

race/ethnicity indicator variables are based on the questions “Are you of

Hispanic or Latino origin?” and “What is your race? [white/black or African

American/American Indian or Native American/Asian or Pacific Islander]”.

Age is self-reported age and Male is an indicator taking the value of 1 if the

respondent is a male and 0 for a female. Job is the response to the question

“Do you currently have a job?” College is an indicator variable taking the

value 1 if the respondent completed at least one year of college and 0 for no

college. It is based on the question “What is the highest grade or year of

regular school you completed?” Married and Divorced are dummies derived

from the population subset that have married and answered “Are you still

married?” Religiosity relies on “To what extent are you a religious person?”

and takes a value between 0 and 3 for very religious. Welfare is a dummy

for “Are you receiving welfare?” Medication is a dummy for “In the past 12

months, have you taken any prescription medication—that is, a medicine

that must be prescribed by a doctor or nurse?” DRD4 is the number of r7

alleles (0, 1, or 2) as opposed to r4 alleles. DRD2 is the number of a2 alleles

(0, 1, or 2) as opposed to a1 alleles. DAT1 is the number of r9 alleles (0, 1,

or 2) as opposed to r10 alleles. MAOA is the number of “High” alleles (0,

1, or 2) as opposed to “Low” alleles. rs2304297 is the number of G alleles

(0, 1, or 2) for this SNP on CHRNA6 (as opposed to C alleles). rs892413 is

the number of C alleles (0, 1, or 2) for this SNP on CHRNA6 (as opposed

to A alleles). rs4950 is the number of G alleles (0, 1, or 2) for this SNP on

CHRNB3 (as opposed to A alleles). rs13280604 is the number of G alleles

(0, 1, or 2) for this SNP on CHRNB3 (as opposed to A alleles). rs2020933


38

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

is the number of A alleles (0, 1, or 2) for this SNP on SLC6A4 (as opposed

to T alleles).

Principal Component 1-10 is the individual loading for each individual

on the 10 principal components associated with the 10 largest eigenvalues

of a singular value decomposition of the subject-genotype matrix. These 10

values contain information about population structure, so including them

in an association test helps to control for population stratification (Price,

Patterson, Plenge, Weinblatt, Shadick, and Reich, 2006). Because principal

component analysis assumes independent observations, we did not use our

entire (family-based) FHS sample to construct the principal components.

Instead we used a subsample of 2,507 unrelated individuals to calculate the

principal components of the genotypic data and then projected the other

individuals in the sample onto those principal components, thus obtaining

the loadings of each individual on each of the top 10 principal components.


39

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

TABLE IV

ACE twin models of life satisfaction, by gender

Life satisfaction (females) Fit statisticsa2 c2 e2 ep -2ll df AIC diff -2ll diff df p

ACE 0.205 0.050 0.745 4 919.8 388 143.8 - - -AE 0.263 - 0.737 3 919.8 389 141.8 0.05 1 0.83CE - 0.205 0.795 3 920.3 389 142.3 0.53 1 0.47E - - 1 2 928.0 390 148.0 8.24 2 0.02

Life satisfaction (males) Fit statisticsa2 c2 e2 ep -2ll df AIC diff -2ll diff df p

ACE 0.389 0.000 0.611 4 951.3 400 151.3 - - -AE 0.389 - 0.611 3 951.3 401 149.3 0 1 1CE - 0.308 0.692 3 955.0 401 153.0 3.73 1 0.05E - - 1 2 972.4 402 168.4 21.11 2 0


40

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

Figure 3.— Longitudinal cross-twin correlations

0

0.1

0.2

0.3

0.4

0.5

0.6

Wave 1 happy Wave 2 happy Wave 3 life satisfaction Wave 4 happy

Heritability estimate

MZ twins correlation

DZ twins correlation


41

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

TABLE V

OLS models of association between 5-HTTLPR and lifesatisfaction that include available Add Health genetic markers.

Model 1 Model 2 Model 3Coeff. SE P-value Coeff. SE P-value Coeff. SE P-value

5-HTTLPR: long 0.061 0.026 0.021 0.066 0.025 0.009 0.080 0.032 0.011MAOA: high -0.014 0.022 0.518 -0.020 0.021 0.336 -0.017 0.027 0.528DRD4: r7 -0.000 0.033 0.993 0.001 0.032 0.970 0.024 0.039 0.548DRD2: a2 0.008 0.030 0.777 -0.000 0.029 0.991 0.048 0.036 0.243DAT1: r10 0.043 0.032 0.169 0.045 0.030 0.135 0.047 0.036 0.191rs2304297: G -0.028 0.061 0.647 -0.012 0.059 0.842 0.018 0.085 0.837rs892413: C -0.010 0.051 0.837 -0.024 0.050 0.628 -0.024 0.073 0.744rs4950: G -0.042 0.065 0.520 -0.039 0.062 0.528 0.006 0.077 0.939rs13280604: G 0.066 0.065 0.035 0.036 0.061 0.555 0.013 0.073 0.863Black -0.138 0.065 0.035 -0.150 0.065 0.020Hispanic 0.294 0.160 0.067 0.256 0.147 0.081Asian -0.205 0.083 0.014 -0.250 0.079 0.002Age 0.006 0.010 0.581 -0.009 0.010 0.390 -0.027 0.013 0.033Male 0.046 0.037 0.212 0.061 0.037 0.097 0.039 0.046 0.384Job 0.108 0.046 0.019 0.104 0.057 0.071College 0.154 0.037 0.000 0.245 0.048 0.000Married 0.218 0.048 0.000 0.259 0.057 0.000Divorced -0.285 0.154 0.065 -0.265 0.159 0.096Religiosity 0.160 0.020 0.000 0.131 0.026 0.000Welfare -0.205 0.112 0.068 -0.100 0.165 0.546Medication -0.072 0.037 0.053 -0.129 0.046 0.005Intercept 3.960 0.246 0.000 3.932 0.243 0.000 4.227 0.304 0.000N 1939 1910 1110R2 0.015 0.087 0.102

Note: Variable definitions are in the Appendix. Standard errors (SE) and P-values are also presented.


42

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

Summary Statistics

TABLE VI

Sample means.

Mean Std Dev Min MaxLife satisfaction 4.20 0.79 1 55-HTTLPR long 1.14 0.72 0 2Age 21.9 1.7 18 26Religiosity 1.43 0.92 0 3

TABLE VII

Percentage of subjects exhibiting these characteristics.

PercentWhite 70.9Black 19.0Hispanic 14.7Asian 8.2Male 47.8College 54.9Married 17.3Divorced 1.4Welfare 4.2Medication 61.2


43

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

Figure 4.— Distribution of life satisfaction00

050050

05001,000

1,00

01,0001,500

1,50

01,500Observations

Obse

rvat

ions

Observationsv. dissatisfied

v. dissatisfied

v. dissatisfieddissatisfied

dissatisfied

dissatisfiedneither

neither

neithersatisfied

satisfied

satisfiedv. satisfied

v. satisfied

v. satisfiedHow satisfied are you with your life as a whole?

How satisfied are you with your life as a whole?


Figure 5.— Distribution of life satisfaction, by zygosity

0

10

20

30

40

50

60

v. dissatisfied dissatisfied neither satisfied v.satisfied


Freq

uen

cy (

%)

MZDZ


44

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

TABLE VIII

Cross-tabs

5-HTTLPR longLife satisfaction 0 1 2 TotalVery dissatisfied 4 4 5 13

(0.8%) (0.3 %) (0.6 %) (0.5%)Dissatisfied 17 35 13 65

3.3% 2.9% 1.6% 2.6%Neither 72 149 97 318

14.2% 12.6 % 11.3 % 12.4%Satisfied 226 544 394 1,164

44.4% 45.9% 45.7% 45.6%Very satisfied 190 453 353 996

37.3% 38.2 % 41.0 % 39.0%Total 509 1,185 862 2,556

100% 100% 100% 100%(20%) (46%) (34%)

TABLE IX

Life satisfaction and genotype, by race

Race MeanWhite

Life satisfaction 4.245-HTTLPR long 1.12

BlackLife satisfaction 4.135-HTTLPR long 1.47

HispanicLife satisfaction 4.225-HTTLPR long 0.93

AsianLife satisfaction 4.015-HTTLPR long 0.69


45

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

12 12

13 13

14 14

15 15

16 16

17 17

18 18

19 19

20 20

21 21

22 22

23 23

24 24

25 25

26 26

27 27

28 28

29 29

TABLE X

Potential mediators

5-HTTLPR longDV p− value

Job 0.17College 0.99Married 0.33Divorced 0.16Religiosity 0.48Welfare 0.25Medication 0.08

Note: Table presents p values for 5-HTTLPR long in models with job, collegeattendance, married, divorced, religious, welfare, and medication as dependentvariables. Regressions also include race, age, and gender controls.


Genes, Economics, and Happiness - Columbia University · 2019-01-10 · e ect on happiness, or only...

Documents

Transcript of Genes, Economics, and Happiness - Columbia University · 2019-01-10 · e ect on happiness, or only...